Vehicle axle detection from under-sampled signal through compressed-sensing-based signal recovery

In traffic data collection, sampling design should satisfy the requirements of identifying prominent pulses corresponding to vehicle axle passage. Insufficient measurement leads signal distortion and attenuation, reducing quality pulses. This study exploits value under-sampled by applying compressed sensing (CS) methods recover components that are critical for detection. Two CS investigated in this strain from inside-pavement instrumented sensors at high-speed traversals. The successfully recovered all axles truck used testing. A comparison measured distances with reference measurements validated effectiveness recovery methods. Therefore, have potential cost, energy consumption, storage space, improving transmission efficiency practical implementations enabling devices designed static achieve dynamic measurements.